R/cheese.R

In ipsRdbs: Introduction to Probability, Statistics and R for Data-Based Sciences

#' Testing of cheese data set  
#' 
#' @format A data frame with 30 rows and 5 columns
#' \describe{
#'  \item{Taste}{A measure of taste quality of cheese}
#'  \item{AceticAcid}{Concentration of Acetic acid}
#'  \item{H2S}{Concentration of hydrogen sulphide}
#'  \item{LacticAcid}{Concentration lactic acid}
#'  \item{logH2S}{Logarithm of H2S}
#' }
#' @examples
#' data(cheese)
#' summary(cheese)
#' pairs(cheese)
#' cheese.lm <- lm(Taste ~ AceticAcid +  LacticAcid + logH2S, data=cheese, subset=2:30)
#' # Check the diagnostics 
#' plot(cheese.lm$fit, cheese.lm$res, xlab="Fitted values", ylab = "Residuals")
#' abline(h=0)
#' # Should be a random scatter
#' qqnorm(cheese.lm$res, col=2)
#' qqline(cheese.lm$res, col="blue")
#' summary(cheese.lm)
#' cheese.lm2 <- lm(Taste ~ LacticAcid + logH2S, data=cheese)
#' # Check the diagnostics 
#' plot(cheese.lm2$fit, cheese.lm2$res, xlab="Fitted values", ylab = "Residuals")
#' abline(h=0)
#' qqnorm(cheese.lm2$res, col=2)
#' qqline(cheese.lm2$res, col="blue")
#' summary(cheese.lm2)
#' # How can we predict? 
#' newcheese <- data.frame(AceticAcid = 300, LacticAcid = 1.5, logH2S=4)
#' cheese.pred <- predict(cheese.lm2, newdata=newcheese, se.fit=TRUE)
#' cheese.pred
#' # Obtain confidence interval 
#' cheese.pred$fit + c(-1, 1) * qt(0.975, df=27) * cheese.pred$se.fit
#' # Using R to predict  
#' cheese.pred.conf.limits <- predict(cheese.lm2, newdata=newcheese, interval="confidence")
#' cheese.pred.conf.limits
#' # How to find prediction interval 
#' cheese.pred.pred.limits <- predict(cheese.lm2, newdata=newcheese, interval="prediction")
#' cheese.pred.pred.limits
"cheese"